Abstract : this paper gives a design method of servo systems input signal , which is based on exact requires of stepping servo systems and combining of traditional form of input signal . it can be easily realiaed by computers and flexiable adjusted by users . the authentication of this approach is also given 文摘:在基于步进化伺服系统输入信号的实际需求,结合传统伺服系统输入形式,采用曲线拟合的方法,提出一种既便于计算机实现,又可灵活调整的输入信号设计方法,并给出相关的证明。
When we apply fuzzy control to a unknow process , we can " t find good control rule because of we don " t know it " s characteristics . this paper based on an analytic structure of fuzzy controller put forward a method , which combines fuzzy controller and neural network . use neural network to make identification of system and provide necessary message for ls . regard control object as an output part of network . based on message provided by neural network adopt bp algorithm to modify experience rule . thus , improve dynamic process of fuzzy control system . simulation results indicate : this controller has better adaptability for variety of model parameters and can modify original system control rule , makes a output follow system input tracks faster 在对不熟悉的过程进行模糊控制时,由于对过程的不了解,很难得到合适的控制规则.基于模糊控制器的一种解析结构,提出了将模糊控制器与神经网络相结合的方法.由神经网络对系统进行辨识,并为学习系统提供必要的信息,将控制对象视为神经网络的输出部分,采用bp算法根据神经网络提供的信息对经验规则进行修改,从而改善模糊控制系统动态响应.仿真结果表明该控制器对模型参数变化具有较好的适应能力,能够较快地修改系统的原控制规则,使对象输出较快地跟踪系统的输入
To quicken convergence and improve model precision , a new algorithm is presented in this paper , which utilize construct orderliness property of self - organization feature maps ( sofm ) , divide system input space and adopt 1 order or 2 order local model in each subspace individually instead of a global model 为了提高收敛速度和模型精度,本文利用自组织映射网络拓扑有序特性,对系统输入空间进行分割,在子空间中采用多个局部一阶线性模型或二阶模型代替全局模型的局部化方法。